Paper Citation Record · LEDGER

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention

As of 21 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2501.15423.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.15423 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:21:16.909166Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:21:16.827241Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T14:21:16.984592Z

Reference resolution

20 of 20 outbound references displayed

verified exact3
verified fuzzy10
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 418e218e-c18a-482a-a861-5d52737540fe · outbound

This paper cites Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:21:16.988730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b22743a-464f-46a0-a566-f2590c6a7de5 · outbound

This paper cites Our frame- work builds upon the MSCSA [6] and nnU-Net [8, 9], allow- ing seamless integration with the broader U-Net family.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Our frame- work builds upon the MSCSA [6] and nnU-Net [8, 9], allow- ing seamless integration with the broader U-Net family

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.260446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d704885-87be-46e7-87c2-3f78df66c593 · outbound

This paper cites an unresolved cited work.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:21:17.143943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation be535bc1-fec0-4683-8573-661d3b4dd9b7 · outbound

This paper cites MSCSA demon- strated improved efficacy in detecting and segmenting small lesions while maintaining competitive performance with a wide variety of training schemes for large lesions.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention MSCSA demon- strated improved efficacy in detecting and segmenting small lesions while maintaining competitive performance with a wide variety of training schemes for large lesions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.133038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64e9f08c-951d-44c6-a2c7-c03638fc58b1 · outbound

This paper cites an unresolved cited work.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:21:17.120880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation deb15ee8-c22d-43d0-ad48-32d37fcf0b23 · outbound

This paper cites Ethical approval was not required as con- firmed by the license attached with the open access data.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Ethical approval was not required as con- firmed by the license attached with the open access data

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.110136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d726f564-79b4-4dd6-b6e6-7bed7b943cb2 · outbound

This paper cites U-net: Convolutional networks for biomedical im- age segmentation,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention U-net: Convolutional networks for biomedical im- age segmentation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.098670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 340ba36b-7f91-4f29-8f1b-5083845e2437 · outbound

This paper cites Attention is all you need,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Attention is all you need,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T14:21:16.857776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc94b2f2-4567-46a6-ad3d-213cc36780b4 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:21:16.862526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eaf8a11-8460-436b-8fa6-0d25666cf2b1 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T14:21:16.867502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a35e7dd4-5e15-4774-b544-282682b760ee · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Unetr: Transformers for 3d medical image segmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.073339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f34d5498-27b8-4bdc-955d-20c476ba20ad · outbound

This paper cites Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:21:16.962533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6bfa78b5-9797-47a0-9a31-289c29fb5a61 · outbound

This paper cites A large, curated, open-source stroke neuroimaging dataset to improve le- sion segmentation algorithms,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention A large, curated, open-source stroke neuroimaging dataset to improve le- sion segmentation algorithms,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.062378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b80b7923-50f6-4eac-8ed5-8c5808280829 · outbound

This paper cites MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:21:16.946696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 54f2cff3-f11d-4de3-88eb-6da9bb0201bf · outbound

This paper cites nnu-net: a self- configuring method for deep learning-based biomedical image segmentation,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention nnu-net: a self- configuring method for deep learning-based biomedical image segmentation,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:21:16.890048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f97d0f8c-826f-46e8-8894-e58a94b47dde · outbound

This paper cites Multi-scale high-resolution vision trans- former for semantic segmentation,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Multi-scale high-resolution vision trans- former for semantic segmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.043565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0bcb7791-cfc2-486a-8e5a-5fe19c59e87a · outbound

This paper cites A probabilistic atlas and reference system for the human brain: Inter- national consortium for brain mapping (icbm),.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention A probabilistic atlas and reference system for the human brain: Inter- national consortium for brain mapping (icbm),

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.032166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 833fee7a-6b31-4706-af1d-7f2b4c1176d0 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Pytorch: An imperative style, high-performance deep learning library,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:21:16.901482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6df6649-e00f-4df6-8d3b-18646e9245de · outbound

This paper cites Segmenting small stroke lesions with novel labeling strategies,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Segmenting small stroke lesions with novel labeling strategies,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.013537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0fc80455-7b91-4ffe-a00a-585f9c72addf · outbound

This paper cites Icpr 2024 competition on multiple sclerosis lesion segmentation—methods and results,.

Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Icpr 2024 competition on multiple sclerosis lesion segmentation—methods and results,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:21:17.001209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 418e218e-c18a-482a-a861-5d52737540fe · inbound